Data-driven stochastic optimization framework that leverages big data in design and operation of power generation units is proposed. A k-mean clustering algorithm is adopted to generate uncertainty scenarios for the stochastic optimization framework. In order to do this, the power generating design and operations problem is formulated as two stage stochastic programming. The first stage variables are associated with design decision, whereas the second stage variables are associated with unit commitment operation (i.e. scheduling). The historical demand data was first collected and reprocessed. After that, the processed electrical demand (uncertain parameter) is processed and recognized using unsupervised machine learning. K-mean cclustering algorithm is used to produce electrical demand scenario profiles, these scenarios are used as inputs to the stochastic model. The proposed model is formulated as a mixed integer linear programming (MILP) and solved using GAMS. The stochastic data driven method enjoys the following features: it is based on information derived from real data without explicitly knowing the data distribution and it applies the recent advances of data analysis tools (e.g. machine learning) to generate a reduced size data set (i.e. clusters) integrated into mathematical model (i.e. design and planning model) that leads to computationally tractable problem.